Ontology-based Visual Querying withOptiqueVQS: Statoil and Siemens Cases?

Ahmet Soylu1, Martin Giese2, Ernesto Jimenez-Ruiz2, Evgeny Kharlamov3,Rudolf Schlatte2, Christian Neuenstadt4, Ozgur Ozcep4, Hallstein Lie5, Vidar

Klungre2, Sebastian Brandt6, and Ian Horrocks3

1 Norwegian University of Science and Technology, Norwayahmet.soylu@ntnu.no

2 University of Oslo, Norway{martingi, ernestoj, rudi}@ifi.uio.no

3 University of Oxford, UK{evgeny.kharlamov, ian.horrocks}@cs.ox.ac.uk

4 University of Lubeck, Germany{neuenstadt, oezcep}@ifis.uni-luebeck.de

5 Statoil ASA, Norwayhali@statoil.com

6 Siemens AG, Germanysebastian-philipp.brandt@siemens.com

Abstract. In this demo, we present an ontology-based visual querysystem, namely OptiqueVQS, for querying static and dynamic datasources. The demo will be based on industrial scenarios provided byStatoil ASA and Siemens AG.

Keywords: Visual query formulation, Ontology, Stream, Sensors, OBDA

1 Motivation

The operational efficiency and effectiveness of business processes rely on domainexperts’ agility in interpreting data into actionable business information. In atypical value creation scenario, domain experts depend on IT experts to extractand deliver relevant data by translating their information needs into extract-transform-load (ETL) processes. Such a workflow is too time intensive, heavy-weight and inflexible; therefore, domain experts need to extract and analyse thedata of interest directly. Although querying is an essential instrument for meetingad hoc information needs, domain experts do not necessarily have technicalskills and knowledge on databases and formal query languages, such as SQL andSPARQL, to extract data. In this context, visual methods for query formulationundertake the challenge of making querying independent of users’ technical skillsand knowledge on the underlying textual query language and data structure [4].

To this end, we have developed an ontology-based visual query system, namelyOptiqueVQS [17,19,15,1], to enable domain experts to formulate queries on their

? Copyright held by the author(s). NOBIDS 2016.

Fig. 1. OptiqueVQS interface – querying static data.

own with respect to an expressive and intelligible domain vocabulary provided byan ontology. OptiqueVQS allows querying static and dynamic data (i.e., stream-temporal) and has been developed within an industrial project, called Optique[5,7,8,10,11]. Optique offers an end-to-end ontology-based data access (OBDA)platform for Big Data. OBDA technologies [14] virtualise data sources into RDFand enable in-place access to legacy data (e.g., relational) over ontologies withoutduplicating or migrating data into triple stores.

In this demo, we present OptiqueVQS over two industrial scenarios, involvingstatic and dynamic data, provided by Statoil ASA [9] and Siemens AG [12,6]respectively.

2 System Overview

OptiqueVQS is a visual query system (VQS), which is a system of interactionsrather than a visual query language (VQL) with a formal syntax and notation [16].It combines multiple representation and interaction paradigms in a widget-basedarchitecture to address a broad range of user and task types. One can formulatetree-shaped conjunctive queries with aggregation. Expressivity is intentionallycompromised for the sake of usability; more precisely, frequently used query typespresenting less complexity to the users are of priority.

Figure 1 and Figure 2 are examples for querying static [18] and dynamic data[19] sources respectively. OptiqueVQS generates SPARQL for static data andSTARQL [13] for dynamic data. Users formulate queries by selecting concept-relationship pairs from a menu-based widget, and constraining and selecting

Fig. 2. OptiqueVQS interface – querying dynamic data.

attributes from a form-based widget (see Figure 1). Formulated queries arepresented as trees, where typed variables appear as nodes and object propertiesappear as arcs. Dynamic properties are coloured in blue and when one is selectedusers could provide parameters such as slide and window and can select predefinedtemplates (see Figure 2).

The main component of OptiqueVQS’s backend is a graph projector [19].This component uses a technique to extract a suitable graph-like structure froma set of OWL 2 axioms [3,2] and feeds OptiqueVQS’s widgets in order to enablea graph-based navigation over an ontology during query formulation. A datasampler component is used to enrich the underlying ontology with additionalaxioms to capture values from data that are frequently used and rarely changed.This allows presenting attributes in different types, such as sliders, multi-selectboxes, date pickers etc, with respect to the underlying data. Moreover, backendharvests the query log for ranking and suggesting query extensions as a userformulates a query [15].

Finally, a set of user experiments with casual users [17] and domain ex-perts [18,19] has been conducted. The results have revealed high efficiency andeffectiveness.

3 Demonstration Scenario

This demo will be based on anonymised Siemens relational stream sensor datagathered from different appliances, such as steam turbines and generators, andon a public fragment of Statoil data regarding the petroleum activities.

Acknowledgments. This work was funded by the EU FP7 Grant “Optique”(agreement no. 318338).

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